Long Wavelength Infrared (LWIR) images have many uses in industry, military, medicine, and science. For example, nondestructive testing uses thermal imagers for detecting defect locations in manufactured materials, thereby allowing for better quality control. Unmanned Airborne Vehicles (UAV) and security cameras often couple a thermal imager with a visible light (VL) camera to enhance night vision for scouting and to improve automatic threat detection over large distances. Firefighters carry handheld imagers while scouting for critical burn points in burning buildings and possible thermal hazards. Thermographers use high-resolution thermal imagers for detecting inflammation, irregular blood-flow, and tumors.
Natural Scene Statistic (NSS) models describe statistical regularities that are observed on images taken of the natural world. Examples of NSS of visible light images include the 1/f behavior of the amplitude spectrum, the sparse coding characteristic of visual cortical filters in response to natural image stimuli, and the Gaussianity exhibited by visual signals following band-pass filter and adaptive gain control operations. Early cortical processing in higher mammalian visual systems appears to have adapted to these natural statistics, and much research into biological visual functioning has been guided by the “efficient coding” hypothesis, which assumes that visual neurons have adapted to efficiently encode natural visual stimuli.
Images and videos, which may be captured by a thermal imager or by a visible imager, follow statistical regularities known as natural scene statistics. While a variety of content is captured by these imagers, current technologies do not possess the ability to distinguish between visible light and infrared images or videos.